Instructions to use isek-ai/SDPrompt-RetNet-300M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use isek-ai/SDPrompt-RetNet-300M with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="isek-ai/SDPrompt-RetNet-300M", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("isek-ai/SDPrompt-RetNet-300M", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use isek-ai/SDPrompt-RetNet-300M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "isek-ai/SDPrompt-RetNet-300M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "isek-ai/SDPrompt-RetNet-300M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/isek-ai/SDPrompt-RetNet-300M

SGLang

How to use isek-ai/SDPrompt-RetNet-300M with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "isek-ai/SDPrompt-RetNet-300M" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "isek-ai/SDPrompt-RetNet-300M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "isek-ai/SDPrompt-RetNet-300M" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "isek-ai/SDPrompt-RetNet-300M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use isek-ai/SDPrompt-RetNet-300M with Docker Model Runner:
```
docker model run hf.co/isek-ai/SDPrompt-RetNet-300M
```

SDPrompt-RetNet-300M / pytorch_model.bin

p1atdev

Upload RetNetForCausalLM

71b8d08 over 2 years ago

download

history blame contribute delete

1.45 GB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: d3c255577aaacffbb17d5b1d98b91065b3b04cb9208296e379547e6b6818cb9d
Size of remote file:: 1.45 GB
SHA256:: d6cfa695b43645828a426101a2853c1bb0067a37853ec5dab76428bb2328873b

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.